Abstract

Subject of this project is the development of dedicated reconstruction algorithms for PET which take patient movements into account. Positron-emission-tomography (PET) is nowadays a well-established tool for diagnosing, staging and monitoring diseases in patients. However, due to the long acquisition times which are necessary for a PET scan, patient movements are often unavoidable. Patient motion during data acquisition can cause image blurring and even severe artifacts in the reconstructed image, thus posing a significant problem for further diagnosis and treatment. To date, available algorithms have been tested only on synthetic data since they rely on general transformation data of the patient which are currently not practically available. We aim at developing algorithms that work on partial transformation data that will be delivered e. g. by new laser cameras or com-bined PET/MRT machines.